> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cekura.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Metric Sampling

> Reduce metric evaluation costs by evaluating only a percentage of calls

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## Overview

Metric Sampling allows you to reduce computational costs by evaluating metrics on only a subset of your calls. Instead of running every metric on every call, you can configure a sampling rate (e.g., 30%) to evaluate metrics on a representative sample while maintaining statistical accuracy.

<Warning>
  Metric sampling applies to **call logs (observability / live traffic) only**. It does **not** apply to simulation runs — all metrics enabled on an evaluator are always evaluated on every simulation run, regardless of the sampling rate configured in project settings.
</Warning>

## How It Works

The sampling feature uses a deterministic accumulator algorithm that ensures the exact percentage of calls are evaluated over time. For example, with a 30% sampling rate, exactly 30 out of every 100 calls will have the metric evaluated.

When a metric evaluation is skipped due to sampling, a placeholder record is created in your dashboard to maintain audit trails.

## Setting Up Sampling

<Steps>
  <Step title="Configure Project Sampling Rate">
    Navigate to **Settings** in your project and find the **Metric Sampling Rate (%)** field.

    Enter a value between 0-100:

    * `0` = No metrics evaluated (0%)
    * `100` = All metrics evaluated (100%, no sampling)
    * `30` = 30% of calls evaluated
    * `0.1` = 0.1% of calls evaluated

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    <Note>
      The sampling rate applies to all metrics that have sampling enabled. You can control which metrics use sampling in the next step.
    </Note>
  </Step>

  <Step title="Enable Sampling for Individual Metrics">
    Go to the **Metrics** section, find the metric you want to sample, and toggle the **Sampling** option to enable it.

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    <Tip>
      Not all metrics need sampling. Consider enabling sampling for:

      * Computationally expensive metrics (e.g., LLM-based evaluations)
      * Non-critical metrics used for trends rather than individual call analysis
      * Metrics evaluated on high-volume agents

      Keep sampling disabled for:

      * Critical workflow metrics (Boolean type)
      * Low-cost metrics (e.g., latency, duration)
      * Metrics where you need 100% coverage
    </Tip>
  </Step>
</Steps>

## Understanding Sampled Results

When a metric evaluation is skipped due to sampling:

* A placeholder record appears in your dashboard
* The explanation shows: `"Sampling skipped"`
* No score is computed for that specific call
* The audit trail is preserved

When a metric is evaluated (passes the sampling threshold):

* The metric runs normally
* Full scores and explanations are provided
* The call counts toward your sampling percentage

## Use Cases

**Cost Optimization**: Reduce evaluation costs on high-volume agents while maintaining statistical insights.

**Performance Testing**: Use 100% sampling during development and testing, then reduce to 10-30% in production.

**Trend Analysis**: For metrics used to track trends over time, sampling provides sufficient data without evaluating every call.

***